Support Vector Machine
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Resource Overview
These MATLAB source files implement Support Vector Machine algorithms for classification and regression tasks, providing practical implementation examples for machine learning applications.
Detailed Documentation
Support Vector Machine (SVM) is a widely-used machine learning algorithm for classification and regression analysis. This MATLAB implementation includes essential SVM components such as kernel functions (linear, polynomial, RBF), optimization routines for finding the optimal hyperplane, and parameter tuning mechanisms. The code demonstrates practical implementation of key SVM concepts including maximum margin classification, support vector identification, and kernel trick applications. These source files provide working examples that help understand SVM theory while offering reusable code components for real-world machine learning projects. The implementation covers both binary and multi-class classification scenarios with proper data preprocessing and model evaluation modules.
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